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  • 标题:Mapping the Potential Global Distribution of Red Imported Fire Ant (iSolenopsis invicta/i Buren) Based on a Machine Learning Method
  • 本地全文:下载
  • 作者:Shuai Chen ; Fangyu Ding ; Mengmeng Hao
  • 期刊名称:Sustainability
  • 印刷版ISSN:2071-1050
  • 出版年度:2020
  • 卷号:12
  • 期号:23
  • 页码:10182
  • DOI:10.3390/su122310182
  • 语种:English
  • 出版社:MDPI, Open Access Journal
  • 摘要:As one of the most notorious invasive species, the red imported fire ant (iSolenopsis invicta/i Buren) has many adverse impacts on biodiversity, environment, agriculture, and human health. Mapping the potential global distribution of iS. invicta/i becomes increasingly important for the prevention and control of its invasion. By combining the most comprehensive occurrence records with an advanced machine learning method and a variety of geographical, climatic, and human factors, we have produced the potential global distribution maps of iS. invicta/i at a spatial resolution of 5 × 5 kmsup2/sup. The results revealed that the potential distribution areas of iS. invicta/i were primarily concentrated in southeastern North America, large parts of South America, East and Southeast Asia, and Central Africa. The deforested areas in Central Africa and the Indo-China Peninsula were particularly at risk from iS. invicta/i invasion. In addition, this study found that human factors such as nighttime light and urban accessibility made considerable contributions to the boosted regression tree (BRT) model. The results provided valuable insights into the formulation of quarantine policies and prevention measures.
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